| Machine learning in aerodynamic shape optimization J Li, X Du, JRRA Martins Progress in Aerospace Sciences 134, 100849, 2022 | 229 | 2022 |
| Rapid airfoil design optimization via neural networks-based parameterization and surrogate modeling X Du, P He, JRRA Martins Aerospace Science and Technology 113, 106701, 2021 | 163 | 2021 |
| A B-spline-based generative adversarial network model for fast interactive airfoil aerodynamic optimization X Du, P He, JRRA Martins AIAA scitech 2020 forum, 2128, 2020 | 72 | 2020 |
| Efficient yield estimation of multiband patch antennas by polynomial chaos‐based Kriging L Leifsson, X Du, S Koziel International Journal of Numerical Modelling: Electronic Networks, Devices …, 2020 | 41 | 2020 |
| Aerodynamic inverse design using multifidelity models and manifold mapping X Du, J Ren, L Leifsson Aerospace science and technology 85, 371-385, 2019 | 41 | 2019 |
| Application of multifidelity optimization techniques to benchmark aerodynamic design problems J Ren, AS Thelen, A Amrit, X Du, LT Leifsson, Y Tesfahunegn, S Koziel 54th AIAA aerospace sciences meeting, 1542, 2016 | 34 | 2016 |
| Optimum aerodynamic shape design under uncertainty by utility theory and metamodeling X Du, L Leifsson Aerospace Science and Technology 95, 105464, 2019 | 31 | 2019 |
| Learning high-dimensional parametric maps via reduced basis adaptive residual networks T O’Leary-Roseberry, X Du, A Chaudhuri, JRRA Martins, K Willcox, ... Computer Methods in Applied Mechanics and Engineering 402, 115730, 2022 | 29 | 2022 |
| Efficient model-assisted probability of detection and sensitivity analysis for ultrasonic testing simulations using stochastic metamodeling X Du, L Leifsson, W Meeker, P Gurrala, J Song, R Roberts Journal of Nondestructive Evaluation, Diagnostics and Prognostics of …, 2019 | 22 | 2019 |
| Single-and multipoint aerodynamic shape optimization using multifidelity models and manifold mapping J Nagawkar, J Ren, X Du, L Leifsson, S Koziel Journal of Aircraft 58 (3), 591-608, 2021 | 20 | 2021 |
| Multifidelity modeling by polynomial chaos-based cokriging to enable efficient model-based reliability analysis of ndt systems X Du, L Leifsson Journal of Nondestructive Evaluation 39 (1), 13, 2020 | 19 | 2020 |
| Multifidelity model-assisted probability of detection via Cokriging X Du, L Leifsson NDT & E International 108, 102156, 2019 | 17 | 2019 |
| Efficient uncertainty propagation for MAPOD via polynomial chaos-based Kriging X Du, L Leifsson Engineering Computations 37 (1), 73-92, 2020 | 14 | 2020 |
| Applications of polynomial chaos-based cokriging to aerodynamic design optimization benchmark problems J Nagawkar, LT Leifsson, X Du AIAA Scitech 2020 Forum, 0542, 2020 | 11 | 2020 |
| Novel adaptive sampling algorithm for POD-based non-intrusive reduced order model J Wang, X Du, JR Martins AIAA AVIATION 2021 FORUM, 3051, 2021 | 9 | 2021 |
| Aerodynamic design of a rectangular wing in subsonic inviscid flow by direct and surrogate-based optimization X Du, A Amrit, AS Thelen, LT Leifsson, Y Zhang, ZH Han, S Koziel 35th AIAA Applied Aerodynamics Conference, 4366, 2017 | 9 | 2017 |
| Super resolution generative adversarial networks for multi-fidelity pressure distribution prediction X Du, JR Martins AIAA SCITECH 2023 Forum, 0533, 2023 | 7 | 2023 |
| Adaptive projected residual networks for learning parametric maps from sparse data T O’Leary-Roseberry, X Du, A Chaudhuri, JR Martins, K Willcox, ... arXiv preprint arXiv:2112.07096, 2021 | 7 | 2021 |
| Airfoil Design Under Uncertainty Using Non-Intrusive Polynomial Chaos Theory and Utility Functions X Du, L Leifsson, S Koziel, A Bekasiewicz Procedia Computer Science 108, 1493-1499, 2017 | 7 | 2017 |
| Aerodynamic Design of the RAE 2822 in Transonic Viscous Flow: Single-and Multi-point Optimization Studies A Amrit, X Du, AS Thelen, LT Leifsson, S Koziel 35th AIAA Applied Aerodynamics Conference, 3751, 2017 | 6 | 2017 |
Leifur LeifssonAssociate Professor, School of Aeronautics and Astronautics, Purdue UniversityVerified email at purdue.edu
